IMPORTING USEFUL LIBRARIES¶

imported libraries with alies name

In [119]:
import re 
from bs4 import BeautifulSoup
import requests
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import ydata_profiling 
from ydata_profiling import ProfileReport
import pygwalker as pg
In [26]:
#Importing the url of the source websites from which we extract the data
Url = 'https://www.shiksha.com/engineering/colleges/b-tech-colleges-india?rf=searchWidget&landing=ctp'
In [27]:
#by requests module , asked for permission to scrap the data using requests.get
Scrap_Page = requests.get(Url,headers = {'User-Agent':'Mozilla/5.0'})
In [28]:
#after requests, checked the status code of request. if it is yes , then it shows 200 . and if it is not then it will show 403
Scrap_Page.status_code
Out[28]:
200
In [120]:
#As we know that we need lists to create a dataframe, so first we create an empty lists
College_Name = [] #empty list of college names
Ratings = [] #empty list of ratings
Courses_offered = [] #empty  list of courses offered
Annual_Package = [] # empty list of annual package
Fee_Range = [] #empty list of fee range
Location = [] #empty list of location of college
College_Type = [] #empty list of type of college
Entrance_Exams = [] #empty list of entrance exam for entry in college

#extracted the required tags and classs of the data and created soup for the required data and putted into for loop 

#As we need multiple pages to scrap  through a single website, so we putted the Url into the for loop in range of required pages

for i in range(1 ,25): # as we need 25 pages to scrap
    Url = f'https://www.shiksha.com/engineering/colleges/b-tech-colleges-india-{i}?rf=searchWidget&landing=ctp'
    Scrap_Page = requests.get(Url,headers = {'User-Agent':'Mozilla/5.0'})
    Soup = BeautifulSoup(Scrap_Page.text, 'html.parser')
    Name_Soup  = Soup.find_all('div' , attrs = {'class':'c8ff'})
    Course_Soup  = Soup.find_all('a' , attrs = {'class':'_9865 ripple dark'})
    Multiple_Soup  = Soup.find_all('div' , attrs = {'class':'cd4f _5c64 contentColumn_2'})
    City_Soup = Soup.find_all('span' , attrs = {'class':'_5588'})
    Type_Soup = Soup.find_all('div' , attrs = {'class':'edfa'})
    #After data scrapping , we need to add the data in a list so we are appending the empty lists which already created
    
    for i in Name_Soup:
        College_Name.append(i.text)#by  using loop , extracted the data and added into empty lists
        
    for i in City_Soup:
        Location.append(i.text) #using for loop , extracted the data and added into empty lists
        
    for i in Course_Soup:
        Courses_offered.append(i.text) # by using for loop , extracted the data and added into empty lists
        
    for i in Multiple_Soup:
        z = i.text
        z_= re.findall('Range₹\d+' , z) #here we used 're' library to extract the useful data 
        if z_:
            Fee_Range.append(z_[0])   #egex module , we added the fee range data  to empty list
        else:
            Fee_Range.append(np.nan)
        
    for i in Multiple_Soup:
        a = i.text
        f = re.findall('Courses(\d\.\d)' , a) # again we use 're' to do the same
        if f:
            Ratings.append(f[0])#dded to the empty list of Ratings
        else:
            Ratings.append(np.nan)
        
        
        
    for i in Multiple_Soup:
        y = i.text
        y_ = re.findall('Package₹\d+' , y) #here also used 're' module to extract the useful data
        if y_:
            Annual_Package.append(y_[0])# using 're' , added the data to empty list
        else:
            Annual_Package.append(np.nan)
        
        
    for i in Multiple_Soup:
        x = i.text
        x_=re.findall( r'Exams Accepted(.*?)Fees Range' , x) #again we use 're' to do extract the useful data
        Entrance_Exams.append(x_[0]) # using 're' , added the data to empty list
        
    for i in Type_Soup:
        h = i.text
        h_ = re.findall('Govt|Pvt' , h) # here we use 'or' condition in 're' and extracted the useful data
        if h_:
            College_Type.append(h_[0])     #g list attribute , added the extracted data to empty list
        else:
            College_Type.append(np.nan)
In [121]:
# Now checking the length and content of required lists we created
#first we are checking length using 'len' function
print(len(College_Name))
print(len(Ratings))
print(len(Courses_offered))
print(len(Annual_Package))
print(len(Fee_Range))
print(len(Location))
print(len(College_Type))
print(len(Entrance_Exams))

511
511
833
511
511
511
833
511
In [122]:
#After checking the length , we observed that courses offered and college type  tooks extra data
#so we are selecting(approved and checked)
Courses_Offered = Courses_offered[0:511]
College_Type1 = College_Type[0:511]
In [123]:
# Now we created a Dataframe named as Data using pandas library through our lists
#for this we need to create  dictionary first
Data = {'College Name':College_Name ,
        'College Location':Location ,
        'Rating':Ratings ,
        'College Type':College_Type1 ,
        'Entrance Exams':Entrance_Exams ,
        'Expected Fee':Fee_Range ,
        'Average Package':Annual_Package ,
       'Courses Offered':Courses_Offered}
#now creating Data Frame
College_Data = pd.DataFrame(Data)
College_Data
Out[123]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal Range₹8 Package₹8 24 Courses
1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal Range₹8 Package₹15 18 Courses
2 VIT Vellore Vellore 4.2 Pvt TNEAVITEEECBSE 12thISCTamilnadu 12th +3 Total Range₹5 Package₹7 22 Courses
3 IIT Madras - Indian Institute of Technology Chennai 4.6 NaN JEE MainJEE AdvancedTotal Range₹5 Package₹15 25 Courses
4 DTU - Delhi Technological University Delhi 4.3 NaN JEE MainDASA UGJAC DelhiCBSE 12thISC +3 Total Range₹3 Package₹9 28 Courses
... ... ... ... ... ... ... ... ...
506 JSPM’s Bhivarabai Sawant Institute of Technolo... Pune 4.3 Govt JEE MainMHT CETTotal Range₹2 NaN 8 Courses
507 Bansal College of Engineering Madhya Pradesh - Other 4.1 Pvt JEE MainMP BETotal Range₹2 NaN 6 Courses
508 University Institute of Technology, Jhabua - R... Madhya Pradesh - Other 3.8 NaN JEE MainTotal NaN NaN 7 Courses
509 MET League of Colleges, Bhujbal Knowledge City... Nashik 4.2 NaN JEE MainMHT CETTotal Range₹4 NaN 4 Courses
510 RMD Sinhgad School of Engineering Pune 3.7 Govt JEE MainMHT CETTotal Range₹3 NaN 11 Courses

511 rows × 8 columns

In [124]:
#As we can see that we need to refine the data as it contains some unuseful data
#so we are doing cleaning data for all the required columns by using pandas , type casting and by using by useful functions

College_Data["Courses Offered"] = College_Data["Courses Offered"].str.replace("Courses|Course"," ")
College_Data["Expected Fee"] = College_Data["Expected Fee"].str.replace("Range₹"," ")
College_Data["Average Package"] = College_Data["Average Package"].str.replace("Package₹"," ")
College_Data  ##Again checking the data frame after applying cleaning process
Out[124]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal 8 8 24
1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal 8 15 18
2 VIT Vellore Vellore 4.2 Pvt TNEAVITEEECBSE 12thISCTamilnadu 12th +3 Total 5 7 22
3 IIT Madras - Indian Institute of Technology Chennai 4.6 NaN JEE MainJEE AdvancedTotal 5 15 25
4 DTU - Delhi Technological University Delhi 4.3 NaN JEE MainDASA UGJAC DelhiCBSE 12thISC +3 Total 3 9 28
... ... ... ... ... ... ... ... ...
506 JSPM’s Bhivarabai Sawant Institute of Technolo... Pune 4.3 Govt JEE MainMHT CETTotal 2 NaN 8
507 Bansal College of Engineering Madhya Pradesh - Other 4.1 Pvt JEE MainMP BETotal 2 NaN 6
508 University Institute of Technology, Jhabua - R... Madhya Pradesh - Other 3.8 NaN JEE MainTotal NaN NaN 7
509 MET League of Colleges, Bhujbal Knowledge City... Nashik 4.2 NaN JEE MainMHT CETTotal 4 NaN 4
510 RMD Sinhgad School of Engineering Pune 3.7 Govt JEE MainMHT CETTotal 3 NaN 11

511 rows × 8 columns

In [125]:
#Now we saved the data to our directory by using to_csv 
College_Data.to_csv("DataProject.csv")
pd.read_csv("DataProject.csv") #agaiun opened in read mode for checking
Out[125]:
Unnamed: 0 College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
0 0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal 8.0 8.0 24
1 1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal 8.0 15.0 18
2 2 VIT Vellore Vellore 4.2 Pvt TNEAVITEEECBSE 12thISCTamilnadu 12th +3 Total 5.0 7.0 22
3 3 IIT Madras - Indian Institute of Technology Chennai 4.6 NaN JEE MainJEE AdvancedTotal 5.0 15.0 25
4 4 DTU - Delhi Technological University Delhi 4.3 NaN JEE MainDASA UGJAC DelhiCBSE 12thISC +3 Total 3.0 9.0 28
... ... ... ... ... ... ... ... ... ...
506 506 JSPM’s Bhivarabai Sawant Institute of Technolo... Pune 4.3 Govt JEE MainMHT CETTotal 2.0 NaN 8
507 507 Bansal College of Engineering Madhya Pradesh - Other 4.1 Pvt JEE MainMP BETotal 2.0 NaN 6
508 508 University Institute of Technology, Jhabua - R... Madhya Pradesh - Other 3.8 NaN JEE MainTotal NaN NaN 7
509 509 MET League of Colleges, Bhujbal Knowledge City... Nashik 4.2 NaN JEE MainMHT CETTotal 4.0 NaN 4
510 510 RMD Sinhgad School of Engineering Pune 3.7 Govt JEE MainMHT CETTotal 3.0 NaN 11

511 rows × 9 columns

In [127]:
#first we are checking missing values

College_Data.isna().sum()
Out[127]:
College Name          0
College Location      0
Rating               18
College Type        207
Entrance Exams        0
Expected Fee         12
Average Package      84
Courses Offered       0
dtype: int64
In [128]:
##As  we can see that there are 207 missing values in College type's column 
#and 18 , 12 , 84 missing values in Rating's column , Expected fee's and Average package's column.

#So by usind pandas statistics functions or approach we are filling the missing values.
In [198]:
#as the data is object , so we imputed the  value on the based of mode

College_Data["Rating"].fillna(value = College_Data["Rating"].mode()[0] , inplace=True)
College_Data["College Type"].fillna(value = College_Data["College Type"].mode()[0] , inplace=True)
College_Data["Expected Fee"].fillna(value = College_Data["Expected Fee"].mode()[0] , inplace=True)
College_Data["Average Package"].fillna(value = College_Data["Average Package"].mode()[0] , inplace=True)
College_Data
Out[198]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal 8 8 24
1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal 8 15 18
2 VIT Vellore Vellore 4.2 Pvt TNEAVITEEECBSE 12thISCTamilnadu 12th +3 Total 5 7 22
3 IIT Madras - Indian Institute of Technology Chennai 4.6 Pvt JEE MainJEE AdvancedTotal 5 15 25
4 DTU - Delhi Technological University Delhi 4.3 Pvt JEE MainDASA UGJAC DelhiCBSE 12thISC +3 Total 3 9 28
... ... ... ... ... ... ... ... ...
506 JSPM’s Bhivarabai Sawant Institute of Technolo... Pune 4.3 Govt JEE MainMHT CETTotal 2 4 8
507 Bansal College of Engineering Madhya Pradesh - Other 4.1 Pvt JEE MainMP BETotal 2 4 6
508 University Institute of Technology, Jhabua - R... Madhya Pradesh - Other 3.8 Pvt JEE MainTotal 2 4 7
509 MET League of Colleges, Bhujbal Knowledge City... Nashik 4.2 Pvt JEE MainMHT CETTotal 4 4 4
510 RMD Sinhgad School of Engineering Pune 3.7 Govt JEE MainMHT CETTotal 3 4 11

511 rows × 8 columns

In [130]:
#Again checking for missing values

College_Data.isna().sum()
Out[130]:
College Name        0
College Location    0
Rating              0
College Type        0
Entrance Exams      0
Expected Fee        0
Average Package     0
Courses Offered     0
dtype: int64
In [135]:
#Now checking for duplicate values using pandas

College_Data.duplicated().sum()

#No duplicate values find
Out[135]:
0

Type Casting by checking data info

In [136]:
College_Data.info()  #information about data type , values , size , memory etc

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 511 entries, 0 to 510
Data columns (total 8 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   College Name      511 non-null    object 
 1   College Location  511 non-null    object 
 2   Rating            511 non-null    float64
 3   College Type      511 non-null    object 
 4   Entrance Exams    511 non-null    object 
 5   Expected Fee      511 non-null    int32  
 6   Average Package   511 non-null    int32  
 7   Courses Offered   511 non-null    int32  
dtypes: float64(1), int32(3), object(4)
memory usage: 26.1+ KB
In [137]:
#Type casting of data according to their uses

College_Data["Courses Offered"] = College_Data["Courses Offered"].astype(int)
College_Data["Rating"] = College_Data["Rating"].astype(float)
College_Data["Expected Fee"] = College_Data["Expected Fee"].astype(int)
College_Data["Average Package"] = College_Data["Average Package"].astype(int)

Attributes

In [138]:
College_Data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 511 entries, 0 to 510
Data columns (total 8 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   College Name      511 non-null    object 
 1   College Location  511 non-null    object 
 2   Rating            511 non-null    float64
 3   College Type      511 non-null    object 
 4   Entrance Exams    511 non-null    object 
 5   Expected Fee      511 non-null    int32  
 6   Average Package   511 non-null    int32  
 7   Courses Offered   511 non-null    int32  
dtypes: float64(1), int32(3), object(4)
memory usage: 26.1+ KB
In [139]:
College_Data.describe()  #to  check data on basis of descriptive statistics
Out[139]:
Rating Expected Fee Average Package Courses Offered
count 511.000000 511.000000 511.000000 511.00000
mean 3.967515 7.534247 5.041096 13.95499
std 0.331842 14.227504 2.911821 10.82897
min 1.400000 1.000000 0.000000 1.00000
25% 3.800000 2.000000 4.000000 7.00000
50% 4.000000 4.000000 4.000000 11.00000
75% 4.200000 6.000000 6.000000 17.00000
max 4.800000 100.000000 23.000000 67.00000
In [140]:
#Now we are checking the first five data from data frame using df.head

College_Data.head()
Out[140]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal 8 8 24
1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal 8 15 18
2 VIT Vellore Vellore 4.2 Pvt TNEAVITEEECBSE 12thISCTamilnadu 12th +3 Total 5 7 22
3 IIT Madras - Indian Institute of Technology Chennai 4.6 Pvt JEE MainJEE AdvancedTotal 5 15 25
4 DTU - Delhi Technological University Delhi 4.3 Pvt JEE MainDASA UGJAC DelhiCBSE 12thISC +3 Total 3 9 28
In [141]:
#now checking bottom five data

College_Data.tail()
Out[141]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
506 JSPM’s Bhivarabai Sawant Institute of Technolo... Pune 4.3 Govt JEE MainMHT CETTotal 2 4 8
507 Bansal College of Engineering Madhya Pradesh - Other 4.1 Pvt JEE MainMP BETotal 2 4 6
508 University Institute of Technology, Jhabua - R... Madhya Pradesh - Other 3.8 Pvt JEE MainTotal 2 4 7
509 MET League of Colleges, Bhujbal Knowledge City... Nashik 4.2 Pvt JEE MainMHT CETTotal 4 4 4
510 RMD Sinhgad School of Engineering Pune 3.7 Govt JEE MainMHT CETTotal 3 4 11
In [142]:
#now checking the data type for required columns for visualisation

print(College_Data['College Name'].dtype)
print(College_Data['College Location'].dtype)
print(College_Data['Rating'].dtype)
print(College_Data['College Type'].dtype)
print(College_Data['Entrance Exams'].dtype)
print(College_Data['Expected Fee'].dtype)
print(College_Data['Average Package'].dtype)
print(College_Data['Courses Offered'].dtype)

object
object
float64
object
object
int32
int32
int32
In [143]:
#Now  checking the size(total no. of value counts)
College_Data.size
Out[143]:
4088
In [144]:
#checking shape(rows*columns)

College_Data.shape
Out[144]:
(511, 8)
In [145]:
#checking the columns in our dataframe

College_Data.columns
Out[145]:
Index(['College Name', 'College Location', 'Rating', 'College Type',
       'Entrance Exams', 'Expected Fee', 'Average Package', 'Courses Offered'],
      dtype='object')
In [146]:
#checking the index value

College_Data.index
Out[146]:
RangeIndex(start=0, stop=511, step=1)
In [147]:
#using dtypes attribute on our dataframe

College_Data.dtypes
Out[147]:
College Name         object
College Location     object
Rating              float64
College Type         object
Entrance Exams       object
Expected Fee          int32
Average Package       int32
Courses Offered       int32
dtype: object

Statistical measures

In [148]:
# Finding statistical measures using pandas on our data frame

#mean

College_Data.mean() # only gave mean for the columns which are in int and flaot data type
Out[148]:
Rating              3.967515
Expected Fee        7.534247
Average Package     5.041096
Courses Offered    13.954990
dtype: float64
In [149]:
# medain

College_Data.median()
Out[149]:
Rating              4.0
Expected Fee        4.0
Average Package     4.0
Courses Offered    11.0
dtype: float64
In [150]:
#Standard deviation

College_Data.std()
Out[150]:
Rating              0.331842
Expected Fee       14.227504
Average Package     2.911821
Courses Offered    10.828970
dtype: float64
In [151]:
#Variance

College_Data.var()
Out[151]:
Rating               0.110119
Expected Fee       202.421864
Average Package      8.478700
Courses Offered    117.266598
dtype: float64
In [152]:
#skewness

College_Data.skew()
Out[152]:
Rating            -1.462397
Expected Fee       4.260500
Average Package    2.768802
Courses Offered    2.164579
dtype: float64
In [153]:
#kurtosis

College_Data.kurtosis()
Out[153]:
Rating              7.388762
Expected Fee       18.817405
Average Package     9.171644
Courses Offered     6.285409
dtype: float64

Data Manipulation

In [154]:
#using groupby , analysing the data betweeb college type, fee , and package

Gdata = College_Data.groupby(by = ["College Type"])[["Expected Fee","Average Package"]].agg(["sum"])
Gdata
Out[154]:
Expected Fee Average Package
sum sum
College Type
Govt 887 655
Pvt 2963 1921
In [155]:
#Sorting our dataframe by using .sort on the basis of fees 

Rating_Wise = College_Data.sort_values(by = 'Rating' , ascending =  False)
Top_10Rating = Rating_Wise.head(10)
Top_10Rating
Out[155]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
272 Dr. B.R. Ambedkar Institute of Technology Port Blair 4.8 Pvt CBSE 12thTotal 42 3 7
92 International Institute of Information Technol... Bangalore 4.7 Pvt JEE MainTotal 22 23 1
7 IIT Kanpur - Indian Institute of Technology Kanpur 4.7 Govt JEE MainJEE AdvancedTotal 8 19 9
0 IIT Bombay - Indian Institute of Technology Mumbai 4.6 Govt JEE MainJEE AdvancedTotal 8 8 24
505 I K Gujral Punjab Technical University, Hoshia... Hoshiarpur 4.6 Pvt JEE MainCBSE 12thPSEB 12th +1 Total 3 4 6
240 BVRIT Hyderabad College of Engineering for Women Hyderabad 4.6 Govt TS EAMCETTotal 4 6 13
1 IIT Delhi - Indian Institute of Technology Delhi 4.6 Govt JEE MainJEE AdvancedTotal 8 15 18
264 Shri Vishnu Engineering College for Women West Godavari 4.6 Pvt AP EAMCETCBSE 12thBIEAP +1 Total 3 3 12
482 Gyan Sagar College of Engineering Sagar 4.6 Pvt JEE MainMP BECBSE 12thMPBSE 12th +2 Total 2 4 7
492 Sagar Institute of Science, Technology and Eng... Bhopal 4.6 Pvt JEE MainMP BETotal 2 4 41
In [156]:
#Sorting our dataframe by using .sort on the basis of Average package

Package_Wise = College_Data.sort_values(by  =  'Average Package' , ascending = False)
Top_10Package = Package_Wise.head(10)
Top_10Package
Out[156]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
92 International Institute of Information Technol... Bangalore 4.7 Pvt JEE MainTotal 22 23 1
7 IIT Kanpur - Indian Institute of Technology Kanpur 4.7 Govt JEE MainJEE AdvancedTotal 8 19 9
19 Indian Institute of Technology, BHU, Varanasi Varanasi 4.3 Pvt JEE MainJEE AdvancedTotal 9 18 26
16 IIT Guwahati - Indian Institute of Technology Guwahati 4.5 Govt JEE MainJEE AdvancedAssam CEE +1 Total 8 18 11
11 BITS Pilani - Birla Institute of Technology an... Pilani 4.4 Govt BITSATTotal 20 18 8
28 IIT Mandi - Indian Institute of Technology Mandi 4.3 Pvt JEE MainJEE AdvancedTotal 8 18 51
58 IIIT Delhi - Indraprastha Institute of Informa... Delhi 4.5 Pvt JEE MainDASA UGJAC DelhiUCEED +2 Total 17 17 13
6 IIT Kharagpur - Indian Institute of Technology Kharagpur 4.5 Govt JEE MainJEE AdvancedTotal 8 17 18
14 IIT Hyderabad - Indian Institute of Technology Hyderabad 4.4 Pvt JEE MainJEE AdvancedTS EAMCET +1 Total 8 16 25
361 IIT Jammu - Indian Institute of Technology Jammu 4.3 Pvt JEE MainJEE AdvancedTotal 8 16 11
In [157]:
Fee_Wise = College_Data.sort_values(by  =  'Expected Fee' , ascending = False)
Top_10fee = Fee_Wise.head(10)
Top_10fee
Out[157]:
College Name College Location Rating College Type Entrance Exams Expected Fee Average Package Courses Offered
101 Panjab University Chandigarh 3.8 Govt JEE MainCBSE 12thPSEB 12th +1 Total 100 4 14
458 Indira Gandhi Engineering College (IGEC) Jabalpur 3.4 Pvt JEE MainMP BETotal 97 4 2
409 University Institute of Technology, Bhopal - R... Bhopal 3.6 Pvt JEE MainCBSE 12thMPBSE 12thMP BE +2 Total 89 3 17
140 J. C. Bose University of Science and Technolog... Faridabad 3.7 Govt JEE MainHSTESCBSE 12thHBSE 12th +2 Total 86 4 3
277 Cochin University of Science and Technology, K... Kochi 4.1 Pvt CUSAT CATCATTotal 86 5 9
144 PES College of Engineering Karnataka - Other 3.9 Pvt COMEDK UGETKCETCBSE 12thKarnataka 2nd PUC +2 T... 84 4 7
298 R.N.S. Institute of Technology Bangalore 3.8 Pvt JEE MainCOMEDK UGETKCETDCETCBSE 12thKarnataka ... 78 4 13
44 College of Engineering, Pune Pune 4.3 Pvt JEE MainMHT CETTotal 75 8 12
317 Central Institute of Technology Assam - Other 3.9 Pvt JEE MainJEE AdvancedAssam CEE +1 Total 72 3 10
365 UoH - University of Hyderabad Hyderabad 4.2 Pvt JEE MainTS EAMCETTotal 72 5 9
In [158]:
#Now analysing the data using pivot table

City_Wise = College_Data.pivot_table(values = ["Expected Fee","Average Package"] , index = "College Type" , columns = "College Location")
City_Wise
Out[158]:
Average Package ... Expected Fee
College Location Agartala Agra Ahmedabad Ahmednagar Aizawl Ajmer Aligarh Allahabad Ambala Amethi ... Vadodara Varanasi Vellore Vidisha Vijayawada Virudhunagar Visakhapatnam Warangal Wardha West Godavari
College Type
Govt 4.0 NaN 3.0 3.0 5.0 NaN NaN 9.0 NaN NaN ... NaN NaN NaN NaN 3.0 NaN NaN 4.0 NaN NaN
Pvt 4.0 3.0 7.0 4.0 6.0 4.0 5.0 7.5 5.0 10.0 ... 6.0 9.0 5.0 1.0 NaN 3.0 2.333333 5.0 3.0 3.0

2 rows × 332 columns

In [159]:
College_Data.pivot_table(values = ["Expected Fee","Average Package"] , index = "College Location" , columns = "College Type")
Out[159]:
Average Package Expected Fee
College Type Govt Pvt Govt Pvt
College Location
Agartala 4.0 4.0 4.0 8.000000
Agra NaN 3.0 NaN 19.500000
Ahmedabad 3.0 7.0 6.0 8.000000
Ahmednagar 3.0 4.0 3.0 3.000000
Aizawl 5.0 6.0 1.0 5.000000
... ... ... ... ...
Virudhunagar NaN 3.0 NaN 3.000000
Visakhapatnam NaN 4.0 NaN 2.333333
Warangal 4.0 7.0 4.0 5.000000
Wardha NaN 4.0 NaN 3.000000
West Godavari NaN 3.0 NaN 3.000000

166 rows × 4 columns

Data Visualization

In [161]:
#BAR CHART FOR RATINGS
Data1 = College_Data["Rating"].value_counts()
Data1.plot.bar(width = 0.4 , color = ["red" ,  "green" , "yellow" , "pink" , "orange"])
plt.legend()
plt.title("RATING OF COLLEGES")
plt.xlabel("Rating")
plt.ylabel("No. of Colleges")
plt.show()

Observation: MOstly colleges having a good rating that is above 4.0

In [163]:
# PIE CHART OF TOP TEN COLLEGES ON FEE
Data6 = Top_10fee["Rating"].value_counts()
Data6.plot.pie(autopct='%.0f%%')
plt.title("Ratings of top 10 colleges")
plt.legend(fontsize=5)
plt.show()

Observation: From 10 ten colleges , 10% colleges having rating of 4.3 which is maximum

In [164]:
sns.countplot(x = "College Type", data=College_Data)
plt.title("Type of colleges")
plt.xlabel("Type of College")
plt.ylabel("No. of  Colleges")
plt.show()

Observation: there are 110 govt and 390 are private colleges in the count of 500

In [165]:
#plotting histogram for  analysing the columns of data frame

College_Data['Courses Offered'].hist()
Out[165]:
<Axes: >

Observation: More than 200 colleges are offerering 10 courses

In [187]:
College_Data['Rating'].hist()
Out[187]:
<Axes: >

Observation: there are more than 250 colleges which are having a good rating of 4.0

In [188]:
College_Data['Average Package'].hist()
Out[188]:
<Axes: >

Observation: There are more than 300 colleges who is giving placement of minimum 4 lpa

In [190]:
Top_10Package['Average Package'].hist()
Out[190]:
<Axes: >

Observation : Top 10 colleges giving placement in between 16 to 23 lpa

In [191]:
plt.scatter(College_Data["Average Package"] , College_Data["Rating"] , color = "red")
Out[191]:
<matplotlib.collections.PathCollection at 0x256842b4e10>

Obbservation: We are having a good correlation between Rating and package

In [183]:
## Hist plot of packages and fee
sns.histplot(College_Data["Average Package"])
plt.xlabel("Annual Package")
plt.ylabel("Distribution")
plt.show()

Observation: Maximum colleges are giving placement of 4 lpa

BIVARIATE ANALYSIS

i) Numerical & Numerical a. Scatterplot b. Line plot c. Heatmap for correlation d. Joint plot ii) Numerical & Categorical a. Bar chart b. Violin plot iii) Categorical & Categorical a. Bar chart b. Grouped bar chart

In [171]:
#Visualizing the data using scatter plot between Fee and package
plt.scatter(Top_10Package["Average Package"] , Top_10Package["College Name"] , color = "red")
Out[171]:
<matplotlib.collections.PathCollection at 0x256f708ce10>

Observation: All IIT's are having a good package

In [173]:
sns.heatmap(College_Data.corr() , annot = True , cmap = "Set3" , linecolor = "black" , linewidth = 4)
plt.show()

Observation: There is a good correlation between Rating , fees , package.

In [175]:
fig , ax = plt.subplots(figsize = (15,8))
pd.crosstab(College_Data["College Location"][0:50] , College_Data["College Type"][0:50]).plot(kind = "bar" , ax = ax , width = 0.6 , stacked = True)
plt.title('City vs Type of college')
plt.legend(bbox_to_anchor = (1,1))
Out[175]:
<matplotlib.legend.Legend at 0x256f77d5610>

Multivariate Analysis

In [184]:
# Now plotting the Profile Report  of our whole data

Profile = ProfileReport(College_Data , title = 'College Data')
Profile

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Out[184]:
























In [185]:



     


# saving this file in HTML
Profile.to_file("DATAPROJECT.html")



     






















    
    








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In [186]:



     


## Publishing the data  in tableau form

pg.walk(College_Data)



     






















    
    




































In [ ]: